Capacity is an important tool in decision-making under risk and uncertainty and multi-criteria decision-making. When learning a capacity-based model, it is important to be able to generate uniformly a capacity. Due to the monotonicity constraints of a capacity, this task reveals to be very difficult. The classical Random Node Generator (RNG) algorithm is a fast-running speed capacity generator, however with poor performance. In this paper, we firstly present an exact algorithm for generating a $n$ elements' general capacity, usable when $n < 5$. Then, we present an improvement of the classical RNG by studying the distribution of the value of each element of a capacity. Furthermore, we divide it into two cases, the first one is the case without any conditions, and the second one is the case when some elements have been generated. Experimental results show that the performance of this improved algorithm is much better than the classical RNG while keeping a very reasonable computation time.

翻译：容量是在风险与不确定性决策以及多准则决策中的重要工具。在基于容量的模型学习中，能够一致地生成容量至关重要。由于容量的单调性约束，这项任务变得极具挑战性。经典的随机节点生成器算法是一种运行速度快的容量生成器，但其性能较差。本文首先提出一种精确算法，用于生成$n$个元素的一般容量，适用于$n < 5$的情况。随后，通过研究容量中各元素值的分布，我们对经典RNG算法进行了改进。此外，我们将该情况分为两种：第一种是无任何条件的情况，第二种是部分元素已生成的情况。实验结果表明，改进后的算法在保持相当合理的计算时间的同时，其性能远优于经典RNG算法。